Tidy Tuesday

A quick and dirty test of Highcharter.

Libraries

library(tidyverse)
library(highcharter)

Read the data

life_expectancy <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-12-05/life_expectancy.csv')
life_expectancy_different_ages <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-12-05/life_expectancy_different_ages.csv')
life_expectancy_female_male <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-12-05/life_expectancy_female_male.csv')

Map of 2021 life expectancy

mapdata <- get_data_from_map(download_map_data("custom/world"))
life_expectancy_2021 <- life_expectancy |> filter(Year == 2021, !is.na(Code))

hcmap(
  "custom/world",
  data = life_expectancy_2021,
  value = "LifeExpectancy",
  joinBy = c("iso-a3", "Code"),
    name = "Life expectancy",
  borderColor = "#FAFAFA",
  borderWidth = 0.1,
  tooltip = list(
    valueDecimals = 1,
    valueSuffix = " years"
  )
)

Evolution of life expectancy, 1951 to 2021

life_expectancy_hm <- life_expectancy |> 
filter(Year>=1950 , !is.na(Code))
  
hchart(life_expectancy_hm,
       type = "heatmap",
       hcaes(x = Year, y = Entity, value = LifeExpectancy))

Evolution of life expectancy relative to 2021, 1951 to 2021

life_expectancy_hm_index <- life_expectancy |> 
filter(Year>=1950 , !is.na(Code)) |> 
  group_by(Entity) |> 
  mutate(index_2021  = LifeExpectancy / LifeExpectancy[Year==2021]) |> 
  mutate(le_2021 = LifeExpectancy[Year==2021])

life_expectancy_hm_index$Entity <- factor(life_expectancy_hm_index$Entity) |> 
  fct_reorder(life_expectancy_hm_index$le_2021)
  
hchart(life_expectancy_hm_index,
       type = "heatmap",
       hcaes(x = Year, y = Entity, value = index_2021)) |> 
  hc_colorAxis(
    stops = color_stops(10, viridisLite::inferno(10, direction = -1)),
    type = "logarithmic"
  ) |> 
    hc_size(height = 900)

Change in life expectancy between 1950 and 2021 by country

life_expectancy_comp <- life_expectancy |> filter(Year==1950 | Year == 2021, !is.na(Code)) |> 
  pivot_wider(values_from = LifeExpectancy, names_from = Year, names_prefix = "year_") |> 
  mutate(change = year_2021 - year_1950) |> 
  arrange(change)

life_expectancy_comp$Entity <- factor(life_expectancy_comp$Entity) |> 
  fct_reorder(life_expectancy_comp$change)
  

life_expectancy_comp |>
  ggplot() +
  geom_col(aes(x = Entity, y = change)) +
  coord_flip() +
  theme_minimal() +
  theme(panel.grid = element_blank())